A small introduction page
Since 2014, I have been keeping track of nearly every song I have listened to. I did this using a website called Last FM. Music I have listened to using desktop programs like iTunes and MusicBee are all included, as well as all my Spotify plays. Recording a play is called scrobbling, and this way I have “scrobbled” more than 118.000 played songs since around April 2014, a period of nearly 6 years. Using a package called “scrobbler” I have imported all my scrobbles to R, and saved it as a .csv file.
Last week, my goal was to link these scrobbles to the Spotify features. Using the code by Andrew Walker that can be found here, after some tweaking, I finally managed to do so! Using the Title and Artist from the scrobbles, the ID from Spotify is fetched through the API, that can be used to fetch the features, a process that took about 6 hours in total. This has resulted in very interesting data, that I will try to visualize the coming weeks.
I have been collecting data on my music listening habits since 2014, and I now have a dataset with timestamped songs and the corresponding Spotify features. How has time influenced what music I listen to?
To show you the data I have collected over the years, a table with all the songs, and the how often they have been played.
Last FM: Average number of songs listened to per day over time
Before going into the Spotify features, it is useful to take a look at some generic plots of my music listening habits, to get an idea of the data. One aspect of music listening habits is how much music you listen to. So, I calculated the amount of music I listen to per day, for each date. This is done by dividing the amount of songs listened to by the amount of days that have passed.
As can be seen in plot 1, the amount of music I listen to has been increasing over time. This can be a confounder when analysing how my music listening has changed, because the samples might not be comparable. However, I think for this project this will not be a problem, but it is something to keep in mind.
Last FM: average number of songs listened to per hour
Another interesting thing to show before getting into the Spotify features, is the average number of songs listened to per hour, shown in plot 2. As you can see, the music listening data reflects my living patterns well: At night I listen to the least music, and in the morning it steadily increases. There is a small dip around 18 at dinnertime, and it increases again after.
Spotify: Valence per season
In class, prof. Burgoyne showed us a plot of the average danceability per season. In the winter people listened to less danceable music, and in the summer much more. I wondered if this was replicable with my own music listening data. As you can see, it is! There is a dip in the months after January, and a peak in August. Interesting to note that my music listening habits are influenced by the time of the year, and that this is can actually be seen in the data. This might tell me something about how useful my data and the corresponding Spotify features will be to answer my research questions.
Spotify: How has my music listening changed over time?
Now on to the real data. My research is focussed around the following question: How has my music listening changed over time? In order for this question to even be relevant, it means that my music listening has to have changed over time. On top of that, this also has to be reflected in the data. This is why I decided to calculate the correlation between time and the Spotify features. Since a correlation spans from -1 to 1, many of the correlations are very high. This means that a lot of these features have changed by a lot over time, which means my music listening has changed a lot. I had hoped to see this, since it looks very promising for further analyses!